1. Field of the Invention
This invention relates to the field of automated information processing and particularly to searching large databases comprised of multiple data types.
2. Brief Description of the Prior Art
Large data storage and processing systems, such as are found in Fortune 500 computer data centers, typically store vast amounts of information on mainframes, minicomputers and other servers. A typical mainframe IT site may deal with numerous operating systems, applications programs, job control languages, processing and control routines, disk file storage schemes and files having unique and incompatible data types and data element structures. Data type elements included in copybooks, includes, assembler files, data definitions, vsam files, adabas dbms, and Natural programs will be found along with multiple data types such as those found in COBOL, PL1, assembly languages, etc.
Searching among the multiple data types and elements is cumbersome and slow. It requires very specific knowledge about the data types to be searched, job control language knowledge, and deep understanding of the overall data environment. Even when performed by technicians having considerable skill, the process involves many cycles of CPU processing—which translates to time and money. If multiple data types must be scanned, each type must normally be scanned individually and with each scan utilizing its own set of rules and methods. Understandably, this complexity often results in incomplete searches and missed information In many cases, the results are provided in a form that cannot be further processed on a pc, mac, or Unix based microcomputer computer with conventional tools like Microsoft Word or Excel.
One prior art system provides a corporate repository tool that will allow searches for relationships between jobs, programs, controls, copybooks and procedures. However, this approach does not support the Natural programming language, adabbas, emails, and many other data types. It also does not offer full text retrieval of an actual data element. That is, it deals only with relationships between data elements and does not permit visual inspection of the actual data element text on a browser window.
Another prior art system, described in Canadian patent application Ser. No, CA2292311, published Jun. 17, 2000, provides a tool that can catalog similar types of items by specifying in advance certain classes in certain locations of text elements. This method is not practical when every word of every element in many data types must be analyzed. The tool also does not provide free text searching for cataloged data.
Search tools including Google, Verity and Dtsearch simply index a directory or location to make it free text searchable. They do not hyperlink between the elements and data types. Nor do they offer cross-referencing to permit observation and analysis of the flow within data elements.
The Natural programming language offers a cross reference within its own members—however, it cannot offer cross reference linking among different data types. A search may be run only on the current machine where the Natural system file exists.
Present search options available to IT professionals trying to locate data on a mainframe are user unfriendly, archaic and primitive. The known search tools are incapable of effectively browsing and searching for items or text among multiple data types. The limited capabilities that exist to perform less complicated search tasks are excessively slow. Efforts to resolve the infamous Y2K problem by scanning vast quantities of code and data revealed clearly the importance of more efficient and reliable search tools. There is a need for a search tool that permits operators and clients of large information databases containing multiple data types to search their information quickly, accurately and easily with a web-based interface that provides the capability to jump from program to program and visually see the modules.